2023 GPS Anomalies, NOTAMs, and Aircraft Traffic

The dataset "2023 GPS Anomalies, NOTAMs, and Aircraft Traffic" was collected and generated for the paper "Detecting GPS Anomalies in Aviation Using ADS-B: Correlating Coordinate Gaps and GPS Deviations with NOTAM Warnings." This dataset provides a comprehensive collection of geospatial and temporal data necessary for analyzing potential GPS anomalies in aviation. The data sources include NOTAMs received from the FAA, and GPS information calculated and extracted from the OpenSky Trino ADS-B database. The dataset contains files such as FAA_and_ICAO_locations, Flights_per_Hour_per_Grid, GPS_Jumps_from_Routes (possible spoofing), GPS_Missing_Coordinates (possible jamming), and two sets of NOTAMs (ICAO and USA). Each file includes detailed columns that capture essential attributes and metrics, enabling thorough examination and correlation of GPS anomalies with NOTAM warnings. The FAA_and_ICAO_locations files include 21,383 records with identifiers, coordinates, and detailed facility information. This dataset serves as a reference for analyzing the geographical distribution of aviation facilities. The Flights_per_Hour_per_Grid file, with 74,219,036 records, provides hourly flight movement counts within specified grids, offering insights into air traffic patterns and potential disruptions. The GPS_Jumps_from_Routes data, comprising 5,878,276 records, documents deviations in flight paths, capturing metrics such as distances, speeds, and timestamps. This data is crucial for identifying potential GPS spoofing incidents by analyzing unusual jumps between consecutive data points. The GPS_Missing_Coordinates file, with 53,227 records, highlights periods of missing GPS signals, indicating possible GPS jamming events. This file includes start and end times, distances between known coordinates, and Navigation Integrity Category (NIC) values to assess data quality during null periods. The NOTAM_ICAO_GPS and NOTAM_USA files, with 30,161 and 234,206 records respectively, provide detailed information on NOTAM areas, including geographic extents, active periods, and categories. This allows for a comprehensive analysis of the spatial and temporal correlation between NOTAM warnings and GPS anomalies, facilitating a better understanding of the impact of GPS disruptions on aviation safety and operations. Summary Table Category File Names Total Records Columns FAA and ICAO Locations FAA_and_ICAO_locations.csv.zip FAA_and_ICAO_locations.dpkg.zip 21,383 WKT, id, fid, Location_ID, ICAO_ID, IATA_ID, FAA_Location_Code, Facility_Type, Facility_Name, FAA_New_Location_Code, Coordinates, lat, lon, Region, Country_Code, Country, State_Id, State_Name, City, Location, Effective_Date, Site_Id, ADO, ARTCC_Id, ARTCC_Computer_ID, ARTCC_Name, Tie_In_FSS_Id, Tie_In_FSS_Name, NOTAM_Facility_Id, NOTAM_Service Flights per Hour per Grid Flights_per_Hour_per_Grid-WKT.csv.zip Flights_per_Hour_per_Grid-WKT.dpkg.zip 74,219,036 grid_id, hour, movement_count, geometry GPS Jumps from Routes (possible spoofing) GPS_Jumps_from_Routes-2023.csv.zip GPS_Jumps_from_Routes-2023.dpkg.zip 5,878,276 WKT, id, fid, icao24, callsign, time_before_spoofing, time_of_spoofing, distance, time_difference, speed_m_s, time_start, time_end GPS Missing Coordinates (possible jamming) GPS_Missing_Coordinates-2023.csv.zip GPS_Missing_Coordinates-2023.dpkg.zip 53,227 WKT, id, icao24, callsign, null_start_time, null_end_time, time_of_previous_not_null_coords, time_of_next_not_null_coords, between_coords_distance_m, null_duration_seconds, between_coords_duration_seconds, avg_nic, min_nic, max_nic, start_time, end_time, start_y, end_x, end_y, start_x NOTAM ICAO GPS NOTAM_ICAO_GPS-2023.csv.zip NOTAM_ICAO_GPS-2023.dpkg.zip 30,161 WKT, id, fid, notam_id, category_name, coordinates_center, radius_nm, radius_mod_nm, notam_number, accountability, location_id, icao_id, domestic_text, icao_text, type, category_id, time_start, time_end NOTAM USA NOTAM_USA-2023.csv.zip NOTAM_USA-2023.dpkg.zip 234,206 WKT, id, fid, notam_id, category_name, is_circle, coordinates_polygon, coordinates_center, radius_nm, faa_location_code, is_faa_location, location_id, is_restricted_area, restricted_area_id, restricted_area_code, category_id, message, notam_number, notam_accountability, moa, type, time_start, time_end Details Note that all the files are zipped as CSV. The GPKG version is also available where geographical information is present. 1. FAA_and_ICAO_locations.csv.zip and FAA_and_ICAO_locations.dpkg.zip Total Records: 21,383 Columns: WKT: Well-Known Text representation of a point in the CSV file, or a geometry field in the DPKG file. id: Unique identifier for each record. fid: Feature identifier. Location_ID: Identifier for the location. ICAO_ID: ICAO (International Civil Aviation Organization) identifier. IATA_ID: IATA (International Air Transport Association) identifier. FAA_Location_Code: FAA location code. Facility_Type: Type of facility (e.g., airport, heliport). Facility_Name: Name of the facility. FAA_New_Location_Code: New location code by FAA. Coordinates: Coordinates of the location. lat: Latitude of the location. lon: Longitude of the location. Region: Geographical region of the location. Country_Code: Country code of the location. Country: Country name of the location. State_Id: State identifier. State_Name: Name of the state. City: City name. Location: General location information. Effective_Date: Effective date of the record. Site_Id: Site identifier. ADO: Airport District Office. ARTCC_Id: ARTCC (Air Route Traffic Control Center) identifier. ARTCC_Computer_ID: ARTCC computer identifier. ARTCC_Name: Name of the ARTCC. Tie_In_FSS_Id: Tie-in Flight Service Station identifier. Tie_In_FSS_Name: Name of the Tie-in Flight Service Station. NOTAM_Facility_Id: NOTAM (Notice to Airmen) facility identifier. NOTAM_Service: Indicates if NOTAM service is available (Y/N). 2. Flights_per_Hour_per_Grid-WKT.csv.zip and Flights_per_Hour_per_Grid-WKT.dpkg.zip Total Records: 74,219,036 Columns: grid_id: Identifier for the grid. hour: Timestamp for the hour. movement_count: Number of flights in each 0.5x0.5 degree grid during each hour of year 2023. geometry: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file. 3. GPS_Jumps_from_Routes-2023.csv.zip and GPS_Jumps_from_Routes-2023.dpkg.zip Total Records: 5,878,276 Columns: WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file. id: Unique identifier for each record. fid: Feature identifier. icao24: ICAO 24-bit aircraft address. callsign: Callsign of the aircraft. time_before_spoofing: Timestamp before the spoofing event. time_of_spoofing: Timestamp of the spoofing event. distance: Distance of the jump in meters. time_difference: Time difference between two coordinates in seconds. speed_m_s: Speed in meters per second. time_start: Start time of the record. time_end: End time of the record. 4. GPS_Missing_Coordinates-2023.csv.zip and GPS_Missing_Coordinates-2023.dpkg.zip Total Records: 53,227 Columns: WKT: Well-Known Text representation of a linestring in the CSV file, or a geometry field in the DPKG file. id: Unique identifier for each record. icao24: ICAO 24-bit aircraft address. callsign: Callsign of the aircraft. null_start_time: Start time of missing GPS coordinates. null_end_time: End time of missing GPS coordinates. time_of_previous_not_null_coords: Time of the last known good GPS coordinates before the null period. time_of_next_not_null_coords: Time of the first known good GPS coordinates after the null period. between_coords_distance_m: Distance between the previous and next known good coordinates in meters. null_duration_seconds: Duration of the null period in seconds. between_coords_duration_seconds: Duration between the previous and next known good coordinates in seconds. avg_nic: Average Navigation Integrity Category (NIC) during the period. min_nic: Minimum NIC during the period. max_nic: Maximum NIC during the period. start_time: Human-readable start time of the null period. end_time: Human-readable end time of the null period. start_y: Latitude of the start point. end_x: Longitude of the end point. end_y: Latitude of the end point. start_x: Longitude of the start point. 5. NOTAM_ICAO_GPS-2023.csv.zip and NOTAM_ICAO_GPS-2023.dpkg.zip Total Records: 30,161 Columns: WKT: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file. id: Unique identifier for each record. fid: Feature identifier. notam_id: NOTAM identifier. category_name: Name of the NOTAM category. coordinates_center: Center coordinates of the NOTAM area. radius_nm: Radius in nautical miles. radius_mod_nm: Modified radius in nautical miles. notam_number: NOTAM number. accountability: Accountability of the NOTAM. location_id: Location identifier. icao_id: ICAO identifier. domestic_text: Text of the NOTAM for domestic purposes. icao_text: Text of the NOTAM for ICAO purposes. type: Type of NOTAM. category_id: Identifier for the NOTAM category. time_start: Start time of the NOTAM. time_end: End time of the NOTAM. 6. NOTAM_USA-2023.csv.zip and NOTAM_USA-2023.dpkg.zip Total Records: 234,206 Columns: WKT: Well-Known Text representation of a polygon in the CSV file, or a geometry field in the DPKG file. id: Unique identifier for each record. fid: Feature identifier. notam_id: NOTAM identifier. category_name: Name of the NOTAM category. is_circle: Indicates if the NOTAM area is a circle (1) or not (0). coordinates_polygon: Coordinates of the polygon vertices. coordinates_center: Center coordinates of the NOTAM area. radius_nm: Radius in nautical miles. faa_location_code: FAA location code. is_faa_location: Indicates if it is an FAA location (1) or not (0). location_id: Location identifier. is_restricted_area: Indicates if it is a restricted area (1) or not (0). restricted_area_id: Restricted area identifier. restricted_area_code: Code for the restricted area. category_id: Identifier for the NOTAM category. message: NOTAM message. notam_number: NOTAM number. notam_accountability: NOTAM accountability. moa: Military Operations Area (MOA) identifier. type: Type of NOTAM. time_start: Start time of the NOTAM. time_end: End time of the NOTAM. References Following references were used to update NOTAMs with WKT polygons: airport-data.com. (n.d.). USA airports by FAA code. https://www.airport-data.com/usa-airports/faa-code/A.html DoD. (2019). Flight information publication area planning special use airspace. NATIONAL GEOSPATIAL-INTELLIGENCE AGENCY. https://www.cnatra.navy.mil/assets-global/docs/area-planning-1A-20190815.pdf FAA. (n.d.-a). Airport Data and Information Portal. https://adip.faa.gov/agis/public/#/airportSearch/advanced FAA. (n.d.-b). US ICAO location finder. https://www.notams.faa.gov/common/icao/USA.html FAA. (2017a, September 30). Encodes/decodes—Aeronautical data [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/loc_id_search/Encodes_Decodes/ FAA. (2017b, October 12). Aeronautical information manual—Official guide to basic flight Information and ATC procedures. https://www.faa.gov/air_traffic/publications/media/AIM_Basic_dtd_10-12-17.pdf#page=142 FAA. (2021, July 26). Order JO 7350.9Z - Location identifiers [Template]. https://www.faa.gov/regulations_policies/orders_notices/index.cfm/go/document.information/documentID/1040529 FAA. (2023a). Pilot’s handbook of aeronautical knowledge. https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/phak FAA. (2023b, June 1). Airport Data [Template]. https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/Airport_Data/ FAA. (2024, February 16). Order JO 7400.10F - Special Use Airspace [Template]. https://www.faa.gov/documentLibrary/media/Order/Order_7400.10F_2024_-_final_-signed.pdf ICAO. (2022). North Atlantic (NAT) air navigation plan Volume I (Doc 9634). https://www.icao.int/EURNAT/EUR%20and%20NAT%20Documents/NAT%20Documents/_eANP%20NAT%20Doc9634/Doc9634%20NAT%20eANP%20Vol%20I.pdf ProAirPilot.com. (2024). Complete list of NOTAM abbreviations. https://proairpilot.com/notam-abbreviations.html SkyVector. (n.d.). Search for Airports by ICAO ID or name. https://skyvector.com/airports Following is the reference to our source of GPS and traffic data, the OpenSky ADS-B database Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., & Wilhelm, M. (2014). Bringing up OpenSky: A large-scale ADS-B sensor network for research. IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, 83–94. https://doi.org/10.1109/IPSN.2014.6846743